Credit scoring models play a fundamental role in the risk management practice of most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g., application, behavioral and collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level (i.e., consumer lending, but also increasingly small business lending). This paper analyzes the history and new developments related to credit scoring models. It is found that with the New Basel Capital Accord, credit scoring models have been remotivated and given unprecedented significance. Banks, in particular, and most financial institutions, worldwide, have either recently developed or modified their existing internal credit risk models to conform with the new rules and best practices recently updated in the market. Moreover, the key steps of the credit scoring model's lifecycle (i.e., assessment, implementation and validation) highlighting the main requirement imposed by Basel II have also been analyzed. It is concluded that banks that are willing to implement the most advanced approach to calculate their capital requirements under Basel II will need to increase their attention and consideration of credit scoring models in the near future.

Credit scoring models play a fundamental role in the risk management practice of most banks. Commercial banks' primary business activity is related to extending credit to borrowers, and generating loans and credit assets. A significant component of a bank's risk, therefore, lies in the quality of its assets that needs to be in line with the bank's risk appetite.In order to manage risk efficiently, quantifying it with the most appropriate and advanced tools is an extremely important factor in determining the bank's success.

Credit risk models are used to quantify credit risk at counterparty or transaction level and they differ significantly by the nature of the counterparty (e.g., corporate, small business, private individual, etc.). Rating models have a long-term view (Through-The-Cycle) and have been always associated with corporate clients, financial institutions and public sector. Scoring models, instead, focus more on the short-term (Point In Time) and have been mainly applied to private individuals and, more recently, extended to Small and Medium Sized Enterprises (SMEs). In this paper, we will focus on credit scoring models giving an overview of their assessment, implementation and usage.

Since the 1960s, larger organizations have been utilizing credit scoring to quickly and accurately assess the risk level of their prospects, applicants and existing customers mainly in the consumer lending business. Increasingly, midsize and smaller organizations are appreciating the benefits of credit scoring as well. The credit score is reflected in a number or letter(s) that summarizes the overall risk utilizing available information on the customer. Credit scoring models predict the probability that an applicant or existing borrower will default or become delinquent over a fixed time horizon. The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level.

Financial Risk Management Journal, Retail Customers, Credit Risk Scoring Models, Small and Medium Sized Enterprises, Risk Management, Commercial Banks, Bayesian Methods, Neural Networks, Risk Assessment Process, Credit Management, Retail Markets, Backtesting, Benchmarking.